Fraudulence monitoring system of table game and fraudulence monitoring program of table game

ABSTRACT

A detection system of the present disclosure stores positions and the amount of game tokens that a game participant places on a game table based on a measurement result by a bet chip measuring device in the same persons for each game participant or player positions of the game table. A management control device compares an actual winning rate and a total return amount with figures obtained by a probability statistic calculation at the time of an end of the number of games to determine whether there is a significant difference therebetween and specifies any one of the game participant or the player position, the game table, or a room having the game table where the significant difference is occurring.

CROSS REFERENCE TO RELATED APPLICATION

The present application is based on Japanese Application No. 2017-198037 filed Sep. 21, 2017, the entire contents of which are hereby incorporated by reference.

FIELD

The present disclosure relates to a fraudulence monitoring system of a table game in a game hall, and more particularly, to a technology for detecting a fraudulent act.

BACKGROUND AND SUMMARY

Attempts are being made to prevent various fraudulent acts in a game hall such as a casino. The game hall has surveillance cameras for monitoring fraudulent acts and prevents fraudulence by determining fraudulence during a game and fraudulence by collection or repayment of chips different from a win or lose result using images obtained from the surveillance cameras. Meanwhile, it is suggested to grasp an amount of chips by attaching an IC tag to each chip in order to grasp the number of betted chips and a total amount of betted chips.

PCT/JP 2015/000171 discloses a card game monitoring system which determines whether chips placed on a game table are collected or repaid as a win or lose result by performing an image analysis on a movement of chips and monitors a fraudulent act.

In the game hall such as the casino, the game table is recorded by the surveillance camera, and when the amount of money won at a time by a game participant is large, the recorded VTR is played to confirm whether or not there is a fraudulent act. However, there is a problem that fraudulence such as sophisticated fraudulence acts in a game table and continuous winning of a small amount of money cannot be found by simply detecting that the game participant wins the large amount of money. In addition, the related art does not sufficiently prevent a fraudulence act by collusion of a dealer and a player.

An object of the present disclosure is to prevent damage due to a fraudulent act by continuously monitoring an amount betted by a game participant and win or lose of a game, comparing data of a probability statistic calculation or past statistical data, and detecting a suspicious pattern suspected of being fraudulent.

To solve the above conventional problem, a fraudulence monitoring system of a table game includes: a win and lose determination device which determines win and lose results for each game in a game table; a bet chip measuring device which grasps an amount of game tokens that a game participant bets on the game table in each game; and a management control device which stores the number of games performed on the game table and specifies positions and the amount of game tokens that the game participant places on the game table based on a measurement result by the bet chip measuring device in each game and stores the specified positions and amount of game tokens in each game participant or player positions of the game table, wherein the management control device includes a calculation function which compares results of an actual winning rate and a total return amount of the same person in each game participant or player position with figures obtained by a probability statistic calculation at the time of an end of a predetermined number of games, on the basis of a calculation on a total number of games in which the same person participates in each game participant or player position, a transition of bet chip amounts for each game, a win and lose for each game, and a return amount obtained in each game to determine whether there is a significant difference therebetween, and specifies any one of the game participant or player position, the game table, or a room having the game table where the significant difference is occurring.

The management control device may further include a function which specifies dealers in each game and include a function which specifies a dealer responsible for a series of games in which the significant difference occurs.

The management control device may include a calculation function which factorizes the total return amount in a series of games in which the significant difference occurs to specify whether an occurrence cause of the significant difference in the series of games in which the significant difference occurs is due to the winning rate or due to a magnitude of a betted amount of the bet chip.

The management control device may include a calculation function which stores the bet chip amounts for each game at the bet position having different return rates, and specifies whether the occurrence cause of the significant difference in a series of games in which the significant difference has occurred is associated with the bet position having different return rates.

The management control device may have a plurality of criteria which determine whether there is a significant difference and issue a different kind of warnings for each criterion.

The management control device may have different determination criteria of a different significant difference for the number of games divided by a predetermined rule or for each of the predetermined number of games and issue a different kind of warnings.

The management control device may determine whether there are significant differences for each game sequentially or every time the predetermined number of games is progressed in accordance with the progress of the plurality of games, and issue a warning when the determination criteria on the significant difference exceed.

The management control device may have a plurality of criteria which determine whether there is a significant difference and specify the game participant or the player position having the fraudulence possibility for each criterion to be able to display scores for each different criteria and display scores or ranks for warnings.

The management control device may obtain the figures obtained by the probability statistic calculation used to determine whether there is a significant difference based on a conditional probability calculation when a specific element is a fixed value.

The management control device may further include a function which sequentially grasps the spots from the playing cards or spots of dice from a series of games, and estimates the fraudulence cause in the case in which it is determined that there is a significant difference based on the data of the spots from a series of games and enumerates the fraudulence cause with the possibility.

The management control device may estimate the fraudulence cause determined that there is the significant difference using any one or both of big data on past games or past fraudulent data, and enumerate the cause with the fraudulence possibility.

To solve the above conventional problem, the fraudulence monitoring system of the present invention may adopt the following form. A fraudulence monitoring system of a table game, includes: an income and expenditure grasping device which grasps income and expenditure of a casino side for each game table in a plurality of game tables; and a management control device which stores the number of games performed on each game table and sequentially grasps and stores a transition of the income and expenditure of the casino side in each game, wherein the management control device includes a calculation function which compares results of the income or expenditure of the casino side with figures obtained by a probability statistic calculation at the time of an end of a predetermined number of games, on the basis of a calculation on a total number of games performed on each game table and the transition of the income and expenditure of the casino side in each game to determine whether there is a significant difference therebetween, and specifies any one of the game table or a room having the game table where the significant difference is occurring.

To solve the above conventional problem, the fraudulence monitoring system of the present invention may adopt the following form. A fraudulence monitoring system of a table game, includes: a win and lose determination device which determines win and lose results for each game in a game table; a bet chip measuring device which grasps an amount of game tokens that a game participant bets on the game table in each game; and a management control device which stores the number of games performed on the game table and specifies positions and the amount of game tokens that the game participant places on the game table using a measurement result by the bet chip measuring device in each game and stores the specified positions and amount of game tokens in each game participant or player positions of the game table, wherein the management control device includes artificial intelligence having past data of a fraudulent game taught as training data, and further includes a calculation function which determines whether a significant difference with fraudulence possibility occurs in any one of the game participant or player position, the game table, or a room having the game table at the time of an end of a predetermined number of games, on the basis of a calculation on a total number of games in which the same person participates in each game participant or player position, a transition of bet chip amounts for each game, a win and lose for each game, and a return amount obtained in each game.

The management control device may further include a function which specifies dealers in each game and include a function which specifies the dealer responsible for a series of games in which the significant difference occurs.

The management control device may include a calculation function which factorizes the total return amount in a series of games in which the significant difference occurs to specify whether an occurrence cause of the significant difference in the series of games in which the significant difference occurs is due to the winning rate or due to a magnitude of a betted amount of a bet chip.

The management control device may include a calculation function which stores the bet chip amounts for each game at the bet position having different return rates, and specifies whether the occurrence cause of the significant difference in a series of games in which the significant difference has occurred is associated with the bet position having different return rates.

The management control device may have a plurality of criteria which determine whether there is a significant difference and issue a different kind of warnings for each criterion.

The management control device may have different criteria of a different significant difference for the number of games divided by a predetermined rule or for each of the predetermined number of games and issue a different kind of warnings.

The management control device may have a plurality of criteria which determine whether there is a significant difference and specify the game participant or the player position having the fraudulence possibility for each criterion to be able to display scores for each different criterion and display scores or ranks for warnings.

The management control device may further include a function which sequentially grasps the spots from playing cards or spots of dice from a series of games, and estimates the fraudulence cause in the case in which it is determined that there is a significant difference based on the data of the spots from a series of games and enumerates the fraudulence cause with the possibility.

The management control device may estimate the fraudulence cause in the case in which there is a significant difference using the big data about the past game as an additional item and enumerate the causes with the fraudulence possibility.

To solve the above conventional problem, the fraudulence monitoring system of the present invention may adopt the following form. A fraudulence monitoring system of a table game, includes: an income and expenditure grasping device which grasps income and expenditure of a casino side for each game table in a plurality of game tables; and a management control device which stores the number of games performed on each game table and sequentially grasps and stores a transition of the income and expenditure of the casino side in each game, wherein the management control device includes artificial intelligence having data of a past fraudulent game taught as training data, and further includes a calculation function which compares results of the income or expenditure of the casino side with figures obtained by a calculation of the artificial intelligence having the data of the past fraudulent game taught as the training data on the basis of a calculation on a total number of games performed on each game table and the transition of the income and expenditure of the casino side in each game to determine whether there is a significant difference therebetween, and determines a possibility that fraudulence occur in any one of the game table or a room having the game table where the significant difference is occurring.

To solve the above conventional problem, the fraudulence monitoring system of the present invention may adopt the following form. A fraudulence monitoring program of a table game, includes: a win and lose determining step of grasping win and lose results for each game in a game table; a bet chip grasping step of grasping an amount of game tokens that a game participant bets on the game table in each game; a game progressing, grasping, managing step of storing the number of games performed on the game table and specifying bet patterns of the game tokens that the game participant places on the game table using transition data of a bet chip in the bet chip grasping step in each game and storing the specified bet patterns in each game participant or player positions of the game table; and a step of verifying occurrence possibility of a fraudulence act, wherein the step of verifying the occurrence possibility of the fraudulence act includes a step of calculating an actual winning rate of the same person in each game participant or player position and a step of calculating a total return amount at the time of an end of a predetermined number of games end, on the basis of grasping and calculating a total number of games in which the same person participates in each game participant or player position, a transition of a bet chip amount, and a return amount for each game, a significant different determining step of comparing the obtained winning rate and total return amount with a winning rate and a total return amount based on a probability statistic calculation or past data to determine whether there is a significant difference therebetween in the predetermined number of games, and an abnormal spot determining step of specifying any one of the game participant or player position, the game table, or a room having the game table where the significant difference is occurring.

The step of verifying the occurrence possibility of the fraudulence act may further include a step of specifying dealers in each game and has a step of specifying the dealer responsible for the series of games in which the significant difference occurs in the abnormal spot determining step.

The step of verifying the occurrence possibility of the fraudulence act may further include an artificial intelligence learning step that learns data of a past fraudulent game as training data.

The fraudulence monitoring system of a table game may further include: a device which specifies the game participant of the game table from the information on a face authentication system or the credit card or the like used.

According to the above configuration, it is possible to prevent damage due to a fraudulent act by continuously monitoring an amount betted by a game participant and win or lose of a game, comparing data obtained by a probability statistic calculation or past statistical data, and detecting a suspicious pattern suspected of being fraudulent.

The foregoing and other objects, features, aspects and advantages of the exemplary embodiments will become more apparent from the following detailed description of the exemplary embodiments when taken in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram schematically showing the whole fraudulence monitoring system in a game hall having a game table according to an embodiment of the present disclosure;

FIG. 2 is a perspective view of a chip showing an example of an overlapping state of grasped chips in the embodiment of the present disclosure;

FIG. 3 is a front cross-sectional view of a game token in the embodiment of the present disclosure;

FIG. 4 is a perspective view of a chip showing another example of the overlapping state of grasped chips in the embodiment of the present disclosure;

FIG. 5 is a schematic diagram of a casino system having a plurality of game tables according to the embodiment of the present disclosure;

FIG. 6 is a diagram showing an example of a graph showing a return amount for each game participant according to the embodiment of the present disclosure;

FIG. 7 is a graph showing a winning rate of a game participant according to the embodiment of the present disclosure;

FIG. 8 is another graph showing a winning rate of a game participant according to the embodiment of the present disclosure; and

FIG. 9 is a diagram schematically showing a fraudulence monitoring system in a game table according to another embodiment of the present disclosure.

DETAILED DESCRIPTION OF NON-LIMITING EXAMPLE EMBODIMENTS

An outline of a fraudulence monitoring system 10 of a game table in a game hall will be described below with reference to the accompanying drawings. Here, the table game includes various table games such as baccarat, blackjack, poker, bridge, roulette, and dice. Representatively, a baccarat game will be described below by way of example.

FIGS. 1 and 2 are diagrams showing the whole fraudulence monitoring system 10. The game table 4 is a table for baccarat, and is provided with each bet area 44 for seven game participants 6. Each bet area 44 for each game participant 6 includes each area for a player 441, a banker 442, a tie 443, a player pair (P) 444, and a banker pair (B) 445. Each game participant 6 places game tokens 9 representing a bet amount on each area corresponding to a target to be bet, thereby making a bet.

A win or lose determination device which determines win or lose results for each game in the game table 4 uses a card distribution device 3. The card distribution device 3 is a so-called electronic shoe already used by those skilled in the art, and is programmed with rules of a game in advance and has a structure to make it possible to determine a win or lose of a game by reading information on a playing card to be distributed. For example, in the baccarat game, the win of the banker, the win of the player, the tie (draw) and the pair (ranks of playing cards of a player or banker are the same) are basically determined by ranks of 2 and 3 cards, and the determination result (win or lose result) is displayed by a result display lamp 13. In addition, the card distribution device 3 sends the win or lose result of the determined game to a management control device 50.

However, according to another embodiment, instead of the card distribution device 3, a win or lose determination device may determine a win or lose by image recognition of a drawn playing card using a camera device 2.

The camera device 2 is provided above the game table 4 to photograph the bet area 44. The game table 4 according to the present embodiment is provided with seven player positions 45 (“1”, “2”, “3”, “5”, “6”, “7”, and “8” in FIG. 1), and seven game participants 6 can participate in the game. The camera device 2 photographs the bet areas 44 of all the player positions 45. One camera device 2 does not necessary photograph the bet areas 44 of all the player positions 45, but a plurality of camera devices 2 may photograph all the bet areas 44 in a shared manner.

The game token 9 has a mark, a pattern and/or a color (may also have both of them) which can specify a type (value) of game token 9 on a surface and/or a side of the game token 9, and a bet chip measuring device 19 specifies positions (to be betted), types, and the number of game tokens 9 placed on the bet areas 44 from colors of edges of side surfaces of the game tokens 9 based on the image photographed by the camera device 2. In this case, the bet chip measuring device 19 may be configured to recognize hiding (when a part of one chip is hidden, or when the entire chip is hidden) or the like due to dead spots of the game token 9 using a self-learning function or the like based on the existing artificial intelligence-based computer or control system and a deep learning (structure) technology, thereby accurately grasping the positions and number of game tokens.

In addition, as shown in FIG. 4, the game token 9 may be configured to have a plurality of plastic layers which have different colors and are stacked, a colored layer 121 which is at least disposed in the middle of the plurality of plastic layers, and white layers or light color layers 120 which have the colored layer 121 sandwiched therebetween and are stacked on both sides of the game token 9 to form a stripe pattern on the side surface of the game token 9 in a stacked direction, such that the colored layer can specify a type of game tokens 9. In this case, it is possible to specify types (10 points, 20 points, 100 points, 1000 points, or the like) of game tokens 9 by changing colors (red, green, yellow, blue, or the like) of the colored layer 121.

In such a case, the game token has a multilayered structure stacked as shown in FIG. 4 (compared with the existing chip shown in FIG. 2) and has the striped pattern distinctly formed on the side surface thereof in the stacked direction, such that the bet chip measuring device 19 can easily measure accurately the types and number of game tokens 9.

Alternatively, the structure which detects the position 8 and the type of game token 9 in the bet area 44 is not limited thereto, and for example, as shown in FIG. 3, the game token 9 may be configured to have an RFID 125 embedded in the game token 9, with the RFID 125 storing information which can specify the type of game tokens 9 and an RFID antenna (not shown) which is provided on the game table 4 and reads ID to specify and detect the positions (to be betted), the type, and the number of game tokens 9 placed in the bet area 44.

A game token tray 17 for a dealer 5 to accommodate the game token 9 is provided in front of the dealer 5 of the game table 4. At least a part of the game token tray 17 is embedded in the game table 4. In the game token tray 17, the bet chip measuring device 19 analyzes and measures the game token 9 stacked in a horizontal direction, thereby determining the type and the number of game tokens 9. In this case, the bet chip measuring device 19 may specify the positions (to be betted), the type, and the number of game tokens 9 accommodated in the game token tray 17 from the color or the like of the edge of the side surface of the game token 9 and the like, based on the image photographed by the camera device 2, and for example, as shown in FIG. 3, the game token 9 may be configured to have the RFID 125 embedded in the game token 9, with the RFID 125 storing the information which can specify at least the type of game tokens 9 and the RFID antenna (not shown) which is provided on the game table 4 and reads the ID to specify and detect the positions (to be betted), the type, and the number of game tokens 9 accommodated in the game token tray 17.

The management control device 50 serves to grasp the positions, the type, and the number of game tokens 9 on the game table 4 which are obtained by the bet chip measuring device 19, perform a balance calculation on the casino side in the game table 4 using the win or lose result obtained from the card distribution device 3 as the win or lose determination device, stores the number of games performed on the game table 4, and specifies a position and an amount of game token 9 that the game participant 6 places on the game table 4 based on the measurement result of the bet chip measuring device 19 in each game and stores the specified position and amount of game token 9 in each game participant 6 or player positions 45 of the game table 4.

In addition, the management control device 50 has a calculation function which calculates settlement amounts for each game (balance calculation on the casino side in the game table 4) for each game, and is configured to settle a total amount of game tokens 9 in the game token tray 17 of the dealer 5 of the game table 4 with the betted game token 9 of the game participant 6 after each game and then be able to compare and calculate whether an increase and decrease in the game token corresponding to the settlement is correct. The management control device 50 can verify the total amount of game tokens 9 of the game token tray 17 at all times (or a predetermined time interval).

The management control device 50 stores the total number of games in which the same person for each game participant 6 or player position 45 participates, a transition of bet chip amounts for each game, and a return amount obtained in each game. The return amount is an amount of money obtained by betting of the game participant 6. The management control device 50 calculates a winning rate at the time of the end of each game as shown in FIG. 7 on the basis of the calculation on the total number of games, the transition of the bet amounts for each game, and the return amounts obtained in each game, aggregates the return amount obtained in each game, and obtains results of an actual wining rate and a total return amount of the same person for each game participant 6 or player position 45. The management control device 50 compares the obtained actual winning rate and total return amount with figures obtained by a probability statistic calculation to determine whether there is a significant difference therebetween and specifies any one of the game participant 6 or the player position 45, the game table 4, or a room having the game table 4 where the significant difference is occurring.

Specifically, for example, the management control device 50 may determine that there is a significant difference when the ratio of the winning rate to the total number of games is higher than a predetermined threshold value as compared with the number on the probability statistical calculation, may determine that there is a significant difference when the ratio of the total return amount to the total number of games is higher than a predetermined threshold value as compared with the number on the probability statistical calculation, may determine that there is a significant difference when the ratio of the winning rate to the total return amount is higher than a predetermined threshold value as compared with the number on the probability statistical calculation, may determine that there is a significant difference when the total return amount obtained by one participant is higher than a predetermined threshold value as compared with the number on the probability statistical calculation, or may determine there is a significant difference according to other criteria.

FIG. 5 shows an overall view of a room having a plurality of game tables 4. The management control device 50 stores the total number of games, the transition of the bet amounts for each game, the return amounts obtained in each game for each same person for each game participant 6 or player position 45 in each of the plurality of game tables 4, and obtains the winning rate and the total return amount. In addition, the management control device 50 may store the total number of games, the transition of the bet amounts for each game, the return amounts obtained in each game for each game table 4 and each room, and may obtain the winning rate and the total return amount. By doing so, it is possible to detect cooperative fraudulent acts in the entire game table or the entire room.

The management control device 50 may further have a function which specifies the dealers 5 in each game and may have a function which specifies the dealer 5 responsible for the series of games in which the significant difference occurs. In addition, the management control device 50 stores the total number of games for each dealer 5, the transition of the bet chip amounts for each game, the return amounts obtained in each game and obtains the winning rate and the total return amount, thereby detecting a dealer 5 that is highly likely to lead a fraudulent act.

FIG. 6 is a diagram showing the transition of the total return amounts for each game participant 6 over time, in which it is possible to detect the game participant 6 who is highly likely to do a fraudulent act by comparing the data obtained by the probability calculation or the past statistical data with the total return amounts or by comparing the total return amounts with each other among the game participants 6.

The management control device 50 includes a calculation function which factorizes the total return amount in a series of games in which the significant difference has occurred to specify whether the occurrence cause of the significant difference in a series of games in which the significant difference has occurred is due to the winning rate or due to a magnitude of a betted amount of a bet chip. That is, it is possible to determine whether although the same amount of money is betted upon winning and losing, since the probability of winning is merely high, the total return amount is large or although the probability of winning is not so high, since a large amount of money is betted upon winning and a small amount of money is betted upon losing, the total return amount is large.

In addition, the management control device 50 includes a calculation function which stores the bet chip amounts for each game at the bet position having different return rates, and specifies whether the occurrence cause of the significant difference in a series of games in which the significant difference has occurred is associated with the bet position having different return rates. For example, in the baccarat game, the return rate is twice in the case of the player/banker, 8 times in the case of the tie, and 11 times in the case of the pair. Therefore, it is possible to detect a betting method to win only in a betting side where the possibility of fraudulent act is high and the return rate like the tie or the pair is high. In particular, the tie is established if 10 or continuous picture cards J, Q, and K are in the game, and therefore is famous for a fraudulent act.

Further, the management control device 50 has a plurality of criteria for determining whether there is the significant difference, and issues a different kind of warning for each criterion. For example, if the detected significant difference is a significant difference which can occur at a frequency of about once every three days, a warning of a first stage is issued, if the detected significant difference is a significant difference which can occur at a frequency of about once a week, a warning of a second stage is issued, and if the detected significant difference is a significant difference which can occur at a frequency of about once a month, a warning of a third stage is issued. Upon the detection of the fraudulence, abnormality display lamps 16 provided on the win or lose determination device 3 or both sides of the game table 4 are turned on, and the detection of the fraudulence is output to a casino management department or the like in a wireless or wired manner. A monitor or a lamp which performs a warning or a display in response to the determination result may further be provided in another place. In addition, it may be configured to be able to play the VTR recorded in the camera device 2 when receiving a warning.

FIG. 7 is a graph showing a change in the number of games of a winning rate of a game participant 6. In this case, for example, a winning rate is high in the range of 100th to 200th games, but if the number of games is small, the warning of the first stage is issued because the winning rate is in the range which may occur by the probability calculation. Thereafter, since the wining rate is lowered, the warning disappears once, but the winning rate increases again at a 1000th game. In this case, the winning rate is high regardless of the large number of games, which is an event that rarely occurs by the probability calculation. As a result, the warning of the third stage is issued.

In addition, the management control device 50 has determination criteria having different significant differences for the number of games divided by a predetermined rule or for each of the predetermined number of games and thus issues a different kind of warnings. For example, in the table game using a shuffle playing card like the baccarat, the shuffle playing card in which playing cards corresponding to 8 to 10 decks are shuffled and are accommodated in a box is set in the card distribution device 3 to perform a game. FIG. 8 shows a graph in which data of winning rates divided (of shoe 1S, shoe 2S, and shoe 3S) by box (this is referred to as shoe) of the shuffle playing card used in the game are aggregated. By doing so, it is possible to detect a fraudulent act such as knowing the arrangement order of specific shuffle playing cards in the box that cannot be detected only by monitoring the overall winning rate. In the graph of the upper part of FIG. 8 showing the overall winning rate continuously monitored, it seems that the winning rate is high first and there is no feature later. However, looking at the graphs divided by shoe in the lower part of FIG. 8, in the shoe 3S, it is possible to detect that the winning rate is high and the possibility of the fraudulent act is high. In addition, it possible to detect that the shoe 1S and the shoe 3S become the graph of the same winning rate and the possibility of the fraudulent act is high.

In addition, the management control device 50 determines whether there are significant differences for each game sequentially or every time the predetermined number of games is progressed in accordance with the progress of the plurality of games, and issues a warning when the determination criteria on the significant difference exceed. For example, it is difficult to determine that there is a significant difference even if the winning rate is 70% in the case in which 50 games are performed, but if the winning rate is 70% after 500 games are performed, it is determined that there is a significant difference and the suspicion of the fraudulent act is increased.

In addition, the management control device 50 may calculate an occurrence probability for each game unit and determine whether there is a significant difference or not. If each game unit consists of, for example, 75 games, the management control device 50 calculates the probability of obtaining the actual results of the 1st to the 75th games, then calculates the probability of obtaining the actual result of the 2nd to the 76th games, and calculates the probability for each game unit in the same way, and outputs a warning when the occurrence probability exceeds the judgment criterion of the significant difference. The game unit is not limited to one consisting of 75 games, but may consists of 100 games, 300 games or the like. Further, the management control device 50 may calculate the occurrence probability for each bet type or each box number.

In addition, the management control device 50 has a plurality of criteria which determine whether there is a significant difference and specifies the game participant 6 or the player position 45 having the fraudulence possibility for each criterion to be able to display scores for each different criteria and display scores or ranks for warnings.

In addition, the management control device 50 can obtain the figures obtained by the probability statistic calculation used to determine whether there is a significant difference based on a conditional probability calculation when a specific element is a fixed value. For example, the calculation may be performed by fixing the number of games and using the conditional probability calculation.

The management control device 50 further has a function which sequentially grasps the playing cards or the spots of the dice from a series of games, and can estimate the fraudulence cause in the case in which it is determined that there is a significant difference based on the data of the spots from a series of games and enumerate the fraudulence cause with the possibility. For example, if a winner of a game is recognized based on the data of the spots from a series of games when a figure of the playing card first drawn is 9, it can be estimated that knowing the position of the playing card of 9 is the fraudulence cause.

In addition, the management control device 50 may estimate the fraudulence cause determined that there is the significant difference using any one or both of big data on past games or past fraudulent data, and enumerate the cause with the fraudulence possibility.

In addition, the management control device 50 may include a calculation function which compares results of the income or expenditure of the casino side with figures obtained by a probability statistic calculation at the time of an end of a predetermined number of games, on the basis of a calculation on a total number of games performed on the respective game tables 4 and the transition of the income and expenditure of the casino side in each game to determine whether there is a significant difference therebetween, and specifies any one of the game tables 4 or a room having the game tables 4 where the significant difference is occurring. In this case, it is unnecessary to grasp the bet amounts for each game participant 6, and it is necessary to grasp only the balance of the game token tray.

Further, the fraudulence monitoring system of the table game may further include a device which specifies the game participant 6 of the game table 4 from information on a face authentication system or an ID card, credit card or the like used.

The face authentication system may acquire an image of a face by feature point extraction or the like, and allocate an identity number (ID or the like) to specify the image of the face. When the specified game participant 6 leaves his/her seat and sits on another game table, the face authentication system includes a warning function which informs the another game table of the existence of the specific player. Specifically, it is possible to warn a pit manager managing each game table 4 or a manager of each table.

According to another embodiment of the present invention, the management control device 50 stores the total number of games in which the same person for each game participant 6 or player position 45 participates, a transition of bet chip amounts for each game, and a return amount obtained in each game. The return amount is an amount of money obtained by betting of the game participant 6. The management control device 50 includes artificial intelligence having data of past fraudulent games taught as training data, and further includes a calculation function which determines whether a significant difference with fraudulence possibility occurs in any one of the game participant 6 or player position 45, the game tables 4, or a room having the game tables 4 at the time of an end of a predetermined number of games, on the basis of a calculation on a total number of games, a transition of bet chip amounts for each game, and a return amount obtained in each game

For example, the management control device 50 is a control device of intelligent type, and is an artificial intelligence type or a deep learning structure which grasps the position (the betted position on the player, the banker, or the pair) and the amount (type and number) of the betted game token 9 on the game table 4 and compares the win or lose history and the obtained amount of chips (winning amount) of each game participant 6 from the win or lose results of each game with the statistical data of a large number (big data) of past games to be able to be extracted as a unique situation (set by the casino). Typically, in the situation in which a winning amount equal to or more than any amount of money (a million dollar) is generated or in the situation in which the state in which a bet chip amount betted on a losing side is small and a bet chip amount on a winning side is large is continued in several games at the play position 7 of the game table 4, the management control device 50 having the artificial intelligence type or a deep learning structure compares the situation with the statistical data (big data or the like) of past games to be able to be extracted as a unique situation. In addition, the management control device 50 can compare the statistical data of the past game to determine what the cause of the fraudulent act is.

Since the management control device 50 has the artificial intelligence having data of past fraudulent games taught as training data, it is possible to detect the fraudulent act of the type which is newly detected by the artificial intelligence.

The management control device 50 may estimate the fraudulence cause in the case in which there is a significant difference using the big data about the past game as an additional item and enumerate the causes with the fraudulence possibility.

In addition, in the fraudulence monitoring system according to another embodiment of the present invention, the management control device 50 includes artificial intelligence having data of past fraudulent games taught as training data, and further includes a calculation function which determines whether a significant difference with fraudulence possibility occurs in any one of the game participant 6 or player position 45, the game tables 4, or a room having the game tables 4 at the time of an end of a predetermined number of games, on the basis of a calculation on a total number of games in which the same person participates in each game participant 6 or player position 45, a transition of bet chip amounts for each game, a win or lose for each game, and a return amount obtained in each game.

According to another embodiment of the present disclosure, a fraudulence monitoring program of a table game, including: a win or lose determining step of grasping win or lose results for each game in a game table 4; a bet chip grasping step of grasping an amount of game tokens 9 that a game participant 6 bets on the game table 4 in each game; a game progressing, grasping, managing step of storing the number of games performed on the game table 4 and specifying bet patterns of the game tokens 9 that the game participant 6 places on the game table 4 using transition data of a bet chip in the bet chip grasping step in each game and storing the specified bet patterns in each game participant 6 or player positions 45 of the game table 4; and a step of verifying occurrence possibility of a fraudulence act, wherein the step of verifying the occurrence possibility of the fraudulence act includes a step of calculating an actual winning rate of the same person in each game participant or player position and a step of calculating a total return amount at the time of the end of a predetermined number of games, on the basis of grasping and calculating a total number of games in which the same person participates in each game participant 6 or player position 45, a transition of a bet chip amount, and a return amount for each game, a significant different determining step of comparing the obtained winning rate and total return amount with a winning rate and a total return amount based on a probability statistic calculation or past data to determine whether there is a significant difference therebetween in the predetermined number of games, and an abnormal spot determining step of specifying any one of the game participant or player position, the game table, or a room having the game table where the significant difference is occurring.

The step of verifying the occurrence possibility of the fraudulence act further includes a step of specifying the dealers 5 in each game and has a step of specifying the dealer 5 responsible for the series of games in which the significant difference occurs in the abnormal spot determining step.

The step of verifying the occurrence possibility of the fraudulence act further includes the artificial intelligence learning step that learns the data of the past fraudulent games as the training data.

The fraudulence monitoring system of the table game further includes a device which specifies the game participant 6 of the game table 4 from the information on the face authentication system or the credit card or the like used.

The example of the baccarat is described above, but the table game may also be the roulette as shown in FIG. 9. In the roulette, a kind of game token 9 having different colors is used for each player, and each game participant 6 performs betting by placing the game tokens 9 having different colors in each of the bet areas 54. The camera device 2 grasps the number and the amount of game tokens 9 betted in the bet area 54, and recognizes the colors of the game tokens 9 to grasp the game participants 6 in which the game tokens 9 are placed. The win or lose determination in the roulette is performed by allowing the camera device 2 to recognize a pocket 58 in which a ball 59 enters a wheel 57. Alternatively, the win or lose determination device which is performed by the manual input of the dealer 5 may be provided.

In addition, although not described in detail, the present disclosure may be applied to various table games such as blackjack, poker, bridge, and dice. 

What is claimed:
 1. A fraudulence monitoring system comprising: an electronic card distribution shoe, wherein the electronic card distribution shoe includes a first sensor configured to detect cards being distributed from the electronic card distribution shoe onto a game table, by which detection the electronic card distribution shoe is configured to track each of a plurality of games having been played on the game table and respective win-lose results of the plurality of games; and a processor that is communicatively coupled to the electronic card distribution shoe to obtain the respective win-lose results of the plurality of games from the electronic card distribution shoe; wherein the processor is configured to: track, based on the obtained respective win-lose results, a number of the plurality of games that have been played; for each of at least one game participant or for each of at least one player position at the game table: for each of at least one of the plurality of games played in association with the respective game participant or the respective player position: use at least one second sensor to identify:  a respective amount of game tokens, including game tokens that are hidden from view, that are placed on the game table as a respective bet by the respective game participant or in association with the respective player position; and  a respective position of the game table onto which the identified respective amount of game tokens was placed; based on the identified respective amount of game tokens, the identified respective position, and the respective win-lose result of the respective game, determine a respective return amount obtained by the respective game participant or the respective player position in the respective game; and store the identified respective amount of game tokens and the identified respective position; and at an end of a predetermined number of the at least one game and based on the respective return amounts determined for each of the at least one game: determine a respective winning rate and a respective total return amount for the respective game participant or the respective player position over the predetermined number of the at least one game; and compare at least one numerical property of the determined respective winning rate and the determined respective total return amount to a corresponding at least one numerical property of a winning rate and a total return amount obtained by a probability statistic calculation to determine whether there is a significant difference therebetween.
 2. The fraudulence monitoring system according to claim 1, wherein the processor is configured to: identify respective dealers managing respective ones of the plurality of games; and determine which of a plurality of game participants, which of the dealers, which of a plurality of player positions, which of a plurality of game tables, or which of a plurality of rooms is associated with occurrence of the significant difference.
 3. The fraudulence monitoring system according to claim 1, wherein the processor is configured to factorize the total return amount of the respective game participant in a series of games in which the significant difference occurs to determine whether a cause of the significant difference in the series of games in which the significant difference occurs is due to the respective winning rate of the respective game participant or due to a magnitude of a betted amount.
 4. The fraudulence monitoring system according to claim 1, wherein the processor is configured to, for each of the plurality of games, for each of the at least one game participant or for each of the at least one player position, store an indication of the identified respective amount of the game tokens in association with a respective return rate, and determine whether a cause for the significant difference is one or more of the return rates.
 5. The fraudulence monitoring system according to claim 1, wherein the processor is configured to perform the comparison to determine whether there is the significant difference for each of at least two sequentially played ones of the plurality of games or in response to the predetermined number of the at least one game having been played, and issue a warning when the significant difference is determined to be present.
 6. The fraudulence monitoring system according to claim 1, wherein the processor is configured to determine whether the significant difference is present based on a conditional probability calculation.
 7. The fraudulence monitoring system according to claim 1, wherein the processor is configured to identify the cards or identify spots of dice used in the plurality of games, and determine a cause of the significant difference based on the identified cards or the identified spots.
 8. The fraudulence monitoring system according to claim 1, wherein the processor is configured to identify and output a cause of the significant difference.
 9. The fraudulence monitoring system according to claim 1, wherein the processor is configured to: determine whether the significant difference is present by executing an artificial intelligence program developed with data of a past fraudulent game as training data; and identify the game table from a plurality of game table or a room having the game table where the significant difference occurred.
 10. The fraudulence monitoring system according to claim 9, wherein the processor is configured to: identify respective dealers managing respective ones of the plurality of games; and determine which of a plurality of game participants, which of the dealers, or which of a plurality of player positions, is associated with occurrence of the significant difference.
 11. The fraudulence monitoring system according to claim 9, wherein the processor is configured to factorize the total return amount of the respective game participant in a series of games in which the significant difference occurs to determine whether a cause of the significant difference in the series of games in which the significant difference occurs is due to the respective winning rate of the respective game participant or due to a magnitude of a betted amount.
 12. The fraudulence monitoring system according to claim 9, wherein the processor is configured to, for each of the plurality of games, for each of the at least one game participant or for each of the at least one player position, store an indication of the identified respective amount of the game tokens in association with a respective return rate, and determine whether a cause for the significant difference is one or more of the return rates.
 13. The fraudulence monitoring system according to claim 9, wherein: the processor is configured to perform the comparison to determine whether there is the significant difference according to each of a plurality of predefined criteria; when the significant difference is determined to be present, the processor is configured to identify according to which of the plurality of predefined criteria the significant difference was identified; and the processor is configured to output each of a plurality of predefined warnings, and to select one or more of the plurality of predefined warning for output in response to the determined significant difference depending on according to which of the plurality of criteria the significant difference was determined.
 14. The fraudulence monitoring system according to claim 9, wherein: the processor is configured to perform the comparison to determine whether there is the significant difference according to each of a plurality of predefined criteria; when the significant difference is determined to be present, the processor is configured to identify according to which of the plurality of predefined criteria the significant difference was identified; and the criteria includes a quantification of a difference based on a number of the at least one game divided by a predetermined rule.
 15. The fraudulence monitoring system according to claim 9, wherein: the processor is configured to perform the comparison to determine whether there is the significant difference according to each of a plurality of predefined criteria; when the significant difference is determined to be present, the processor is configured to identify according to which of the plurality of predefined criteria the significant difference was identified; and the processor is configured to determine respective scores for each of the plurality of predefined criteria.
 16. The fraudulence monitoring system according to claim 9, wherein the processor is configured to identify the cards or spots of dice used in the plurality of games, and determine a cause of the significant difference based on the identified cards or spots.
 17. The fraudulence monitoring system according to claim 9, wherein the processor is configured to identify and output a cause of the significant difference.
 18. The fraudulence monitoring system according to claim 1, wherein the processor is configured to: determine whether the significant difference is present by executing an artificial intelligence program developed with data of a past fraudulent game as training data.
 19. The fraudulence monitoring system according to claim 1, wherein the processor is configured to identify each of the at least one game participant using a face authentication, information obtained from an ID card, or information from a credit card that is used.
 20. The fraudulence monitoring system according to claim 1, wherein: the processor is configured to perform the comparison to determine whether there is the significant difference according to each of a plurality of predefined criteria; and when the significant difference is determined to be present, the processor is configured to identify according to which of the plurality of predefined criteria the significant difference was identified.
 21. The fraudulence monitoring system according to claim 20, wherein the processor is configured to select one or more of a plurality of predefined warnings for output in response to the determined significant difference depending on according to which of the plurality of predefined criteria the significant difference was determined.
 22. The fraudulence monitoring system according to claim 20, wherein the criteria includes a quantification of a difference based on a number of the at least one game divided by a predetermined rule.
 23. The fraudulence monitoring system according to claim 20, wherein the processor is configured to determine respective scores for each of the plurality of predefined criteria.
 24. A fraudulence monitoring system comprising: an electronic card distribution shoe, wherein the electronic card distribution shoe includes a first sensor configured to detect cards being distributed from the electronic card distribution shoe onto a game table, by which detection the electronic card distribution shoe is configured to track each of a plurality of games having been played on the game table; at least one second sensor, wherein the at least one second sensor is configured to detect objects on the game table; and a processor that is communicatively coupled to the electronic card distribution shoe and to the at least one second sensor, wherein the processor is configured to: track, based on communication with the electronic card distribution shoe, a plurality of games played at the game table; based on the coupling of the processor to the electronic card distribution shoe and to the at least one second sensor, correlate respective movements of the objects on the table to respective ones of the tracked plurality of games, and thereby identify results regarding a respective income and a respective expenditure of a casino side at the game table for each of the plurality of games; at an end of a predetermined number of the plurality of games, compare the results regarding the identified respective incomes and the identified respective expenditures of the casino side over the predetermined number of the plurality of games with corresponding figures obtained by a probability statistic calculation to determine whether there is a significant difference therebetween according to each of a plurality of predefined criteria; and when the significant difference is determined to be present: identify according to which of the plurality of predefined criteria the significant difference was identified; and identify the game table from a plurality of game tables or a room having the game table where the significant difference occurred.
 25. The fraudulence monitoring system according to claim 24, wherein the processor is configured to identify the results regarding the respective income and the respective expenditure even regarding one or more of the objects that are at least partially hidden from view based on input from the at least one sensor.
 26. The fraudulence monitoring system according to claim 25, wherein the at least one second sensor includes a camera or a Radio Frequency Identification (RFID) sensor.
 27. The fraudulence monitoring system according to claim 24, wherein the at least one second sensor includes a camera or a Radio Frequency Identification (RFID) sensor.
 28. The fraudulence monitoring system according to claim 24, wherein the objects, by the at least one second sensor's sensing of which the processor is configured to identify the results regarding the respective income and the respective expenditure, include game tokens or currency.
 29. A fraudulence monitoring method performed using a system that includes (1) an electronic card distribution shoe that includes a first sensor configured to detect cards being distributed from the electronic card distribution shoe onto a game table, by which detection the electronic card distribution shoe is configured to track each of a plurality of games having been played on the game table and respective win-lose results of the plurality of games, and (2) a processor that is communicatively coupled to the electronic card distribution shoe to obtain the respective win-lose results of the plurality of games from the electronic card distribution shoe, the method comprising the processor performing the following: tracking, based on the respective win-lose results obtained by the processor, a number of the plurality of games that have been played; for each of at least one game participant or for each of at least one player position at the game table: for each of at least one of the plurality of games played in association with the respective game participant or the respective player position: using at least one second sensor to identify: a respective amount of game tokens, including game tokens that are hidden from view, that are placed on the game table as a respective bet by the respective game participant or in association with the respective player position; and a respective position of the game table onto which the identified respective amount of game tokens was placed; based on the identified respective amount of game tokens, the identified respective position, and the respective win-lose result of the respective game, determining a respective return amount obtained by the respective game participant or the respective player position in the respective game; and storing the identified respective amount of game tokens and the identified respective position; and at an end of a predetermined number of the at least one game and based on the respective return amounts determined for each of the at least one game: determining a respective winning rate and a respective total return amount for the respective game participant or the respective player position over the predetermined number of the at least one game; comparing at least one numerical property of the determined respective winning rate and the determined respective total return amount to a corresponding at least one property of a winning rate and a total return amount obtained by a probability statistic calculation to determine whether there is a significant difference therebetween according to each of a plurality of predefined criteria; and when the significant difference is determined to be present, identifying according to which of the plurality of predefined criteria the significant difference was identified.
 30. The fraudulence monitoring method according to claim 29, further comprising: determining which of a plurality of game participants, which of a plurality of player positions, which of a plurality of game tables, or which of a plurality of rooms is associated with occurrence of the significant difference.
 31. The fraudulence monitoring method according to claim 30, further comprising: identifying respective dealers managing respective ones of the plurality of games; and determining which of the dealers is associated with occurrence of the significant difference.
 32. The fraudulence monitoring method according to claim 30, further comprising training an artificial intelligence program using data of a past fraudulent game as training data, wherein the determination of whether the significant difference has occurred is performed using the trained artificial intelligence program. 